Digital Image Processing

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Real Time Face Mask Detection Using CNN for COVI-19


Affiliations
1 Department of CSE, East West Institute of Technology, Bangalore, India
2 Department of CSE, East West Institute of Technology, Bangalore, India
     

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Covid illness 2019 has influenced the world truly. One significant assurance technique for individuals is to wear veils in open regions. Besides, numerous public specialist organizations expect clients to utilize the assistance just in the event that they wear covers accurately. Notwithstanding, there is a couple of exploration learns about-face cover location dependent on picture investigation. We propose Real-Time Face Mask Detection, which is a highexactness and proficient face veil indicator. The proposed Real-Time Face Mask Detection is a one-stage indicator, which comprises of a component pyramid organization to combine undeniable level semantic data with different element maps and a novel setting consideration module to zero in on distinguishing face covers. Moreover, we likewise propose a novel cross-class object expulsion calculation to dismiss forecasts with low confidences and a high convergence of association. Additionally, we likewise investigate the chance of carrying out Real-Time Face Mask Detection with a lightweighted neural organization MobileNet for implanted or cell phones.

Keywords

Data Collection, Data Preprocessing, Face Detection, CNN, E-Mail Based Notification, Sound Generation.
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  • Real Time Face Mask Detection Using CNN for COVI-19

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Authors

N. Supritha
Department of CSE, East West Institute of Technology, Bangalore, India
C. Karan
Department of CSE, East West Institute of Technology, Bangalore, India
Preetam Katti
Department of CSE, East West Institute of Technology, Bangalore, India
J. Surendra
Department of CSE, East West Institute of Technology, Bangalore, India
M. Tharun
Department of CSE, East West Institute of Technology, Bangalore, India

Abstract


Covid illness 2019 has influenced the world truly. One significant assurance technique for individuals is to wear veils in open regions. Besides, numerous public specialist organizations expect clients to utilize the assistance just in the event that they wear covers accurately. Notwithstanding, there is a couple of exploration learns about-face cover location dependent on picture investigation. We propose Real-Time Face Mask Detection, which is a highexactness and proficient face veil indicator. The proposed Real-Time Face Mask Detection is a one-stage indicator, which comprises of a component pyramid organization to combine undeniable level semantic data with different element maps and a novel setting consideration module to zero in on distinguishing face covers. Moreover, we likewise propose a novel cross-class object expulsion calculation to dismiss forecasts with low confidences and a high convergence of association. Additionally, we likewise investigate the chance of carrying out Real-Time Face Mask Detection with a lightweighted neural organization MobileNet for implanted or cell phones.

Keywords


Data Collection, Data Preprocessing, Face Detection, CNN, E-Mail Based Notification, Sound Generation.

References